Cost-effectiveness of insulin pumps compared with multiple daily injections both provided with structured education for adults with type 1 diabetes: a health economic analysis of the Relative Effectiveness of Pumps over Structured Education (REPOSE) randomised controlled trial

Objectives To assess the long-term cost-effectiveness of insulin pumps and Dose Adjustment for Normal Eating (pumps+DAFNE) compared with multiple daily insulin injections and DAFNE (MDI+DAFNE) for adults with type 1 diabetes mellitus (T1DM) in the UK. Methods We undertook a cost–utility analysis using the Sheffield Type 1 Diabetes Policy Model and data from the Relative Effectiveness of Pumps over Structured Education (REPOSE) trial to estimate the lifetime incidence of diabetic complications, intervention-based resource use and associated effects on costs and quality-adjusted life years (QALYs). All economic analyses took a National Health Service and personal social services perspective and discounted costs and QALYs at 3.5% per annum. A probabilistic sensitivity analysis was performed on the base case. Further uncertainties in the cost of pumps and the evidence used to inform the model were explored using scenario analyses. Setting Eight diabetes centres in England and Scotland. Participants Adults with T1DM who were eligible to receive a structured education course and did not have a strong clinical indication or a preference for a pump. Intervention Pumps+DAFNE. Comparator MDI+DAFNE. Main outcome measures Incremental costs, incremental QALYs gained and incremental cost-effectiveness ratios (ICERs). Results Compared with MDI+DAFNE, pumps+DAFNE was associated with an incremental discounted lifetime cost of +£18 853 (95% CI £6175 to £31 645) and a gain in discounted lifetime QALYs of +0.13 (95% CI −0.70 to +0.96). The base case mean ICER was £142 195 per QALY gained. The probability of pump+DAFNE being cost-effective using a cost-effectiveness threshold of £20 000 per QALY gained was 14.0%. All scenario and subgroup analyses examined indicated that the ICER was unlikely to fall below £30 000 per QALY gained. Conclusions Our analysis of the REPOSE data suggests that routine use of pumps in adults without an immediate clinical need for a pump, as identified by National Institute for Health and Care Excellence, would not be cost-effective. Trial registration number ISRCTN61215213.

List of Tables  Table 1: The results of the parametric survival models fitted to patients allocated to the continuous subcutaneous insulin infusion dose adjustment for normal eating arm of the REPOSE trial (n=130) ... 4  The stability of the model results was assessed in terms of the stability of incremental net monetrary benefit (iNMB) using a threshold cost per quality adjusted life year (QALY) of £20,000 per QALY gained. This statistic is useful for the purpose of assessing model stability as, so long as the mean iNMB is statistically significantly different from 0 at the 5% level then the results are sufficiently stable that any decision made as a consequence of them will not change.
The mean incremental net monetary benefit at £20,000 per QALY gained was -£16,201 ( ̅ ). The variance of this was 2.18E+08. The standard error was √2.18 + 8/500 = 661. As the standard error was under 5% of the mean incremental net monetary benefit, 500 probabilistic sensitivity analysis (PSA) runs was determined to be sufficiently robust for making reliable decisions for these comparators 2 Determining the number of simulated individuals to use in each run Likewise to determine the stability of the ICER in the deterministic results and the scenario analyses the stability of the iNMB was assessed over 5,00 simulated individuals. The model was run initially using the same 5,000 simulated individuals in each arm of the deterministic base case. The mean incremental net monetary benefit at £20,000 per QALY gained was -£15,759 ( ̅ ). The variance of this was 1.27E+10. The standard error was √1.27 + 10/5000 = 1,594. As the standard error was approximately 10% of the mean incremental net monetary benefit, 5000 simulated individuals was determined to be sufficient to produce stable results for the analyses.

Other cause mortality
Individuals can also die from other causes. This other-cause mortality was updated in version of the model used for these analyses. Other-cause mortality was calculated using UK life tables from 2012 to 2014 adjusted to exclude the causes either attributed to diabetes mellitus (either type 1, type 2 or unspecified, ICD-10 codes E10-14) or modelled directly in the microvascular and macrovascular disease components (deaths due to: end stage renal disease; myocardial infarction, stroke and heart failure, ICD-10 codes N18, I20-21, I61-64, I50). [1][2][3][4] 4 Probability of death from end stage renal disease This model parameter was altered from the value reported in Heller et al. 5 to reflect directly observable data available in Wolfe et al. 6 on the probability of end stage renal disease. At baseline, 102,163 patients with diabetes were receiving dialysis and over a maximum follow up of 6 years 44,916 of these patients died. This gave a probability of death from end stage renal disease (ESRD) of 44.00% over 6 years. In the model probabilistic sensitivity analysis, the uncertainty in this probability was parameterised using a beta distribution with an alpha parameter of 44,916 and a beta parameter of 57,247. The probability of death from end stage renal disease over 6 years was first converted into an instantaneous rate of death and then yearly probability of death from end stage renal disease using the method in Briggs et al. 7 5 The clinical effectiveness parameters

Treatment switching
The treatment switching curves were used to estimate the incidence of treatment switching in the model in the first and second year. Covariates were used in the parametric models to control for: HbA1c prior to switching, number of diabetic ketoacidosis events (DKAs) and number of severe hypoglycaemic events in the year prior to switching (or at 2 years follow up if no switching occurred). The standard errors of the parametric survival models were adjusted for clustering in each DAFNE course. As separate models were fitted to the insulin pumps + dose adjustment for normal eating (pumps+DAFNE) and the multiple daily injections + dose adjustment for normal eating (MDI+DAFNE) arm, no assumption of proportional hazards or accelerated failure time was made.
The curves were not extrapolated, as the clinical expert opinion of a Professor of Clinical Diabetes & Honorary Consultant Physician and a Professor in Public Health & Health Technology Assessment was that if an individual was still using a pump after two years that they would continue to use pump as in their experience once an adult with type 1 diabetes mellitus (T1DM) was successfully using an insulin pump they were unlikely change their insulin delivery method.
The different parameters of the parametric survival models fitted to the pumps+DAFNE arm is given in Table 1. The equivalent parameters are given for the MDI+DAFNE arm in Table 2.
The goodness of fit of the parametric survival curves were assessed using the Akaike information criterion (AIC), Bayesian information criterion (BIC) and a visual assessment of the survival curves estimated from parametric models plotted against the nonparametric Kaplan-Meier curve . The one and two-year time points are the time points of relevance for assessing the visual fit of the curves in this analysis, as the model uses a yearly time cycle so treatment switching is only predicted in the model at these time points. The AIC and the BIC are given in Table 3. Lower values for these statistics indicate a better model fit. For the pumps+DAFNE arm, the exponential model has the lowest AIC and BIC. For the MDI+DAFNE arm, the Weibull model had the lowest AIC and the exponential model has the lowest BIC. The plot of the parametric survival curves against the underlying Kaplan-Meier curve are provided in Figures 1 and 2. It is clear that the exponential model provides a reasonable fit to the Kaplan-Meier for the pumps+DAFNE arm at the one and two-year time points therefore this curve was used in the base case economic model. It is also clear that for the MDI+DAFNE arm, the exponential curve does not provide a reasonable fit to the Kaplan -Meier curve at one year whereas the Weibull curve provides a reasonable fit at both one and two years. Therefore the Weibull curve was used as the base case curve for the MDI+DAFNE model arm.
The uncertainty in the parametric survival curves was included in the model's probabilistic sensitivity analysis using a multivariate normal distribution. The variance-covariance matrices and the predicted coefficients for each of the parametric survival models were used to parameterise the multivariate normal distributions. The coefficients are given in Table 1and Table 2 respectively and the variancecovariance matrices for each of the parametric curves is given in Tables 4 toTable 13.               parameters of interest, the mean effect and the dispersion on the variance. Treatment allocation, 5 baseline HbA1c and centre were included as covariates for the mean effect on HbA1c after one year. 6 Treatment allocation, baseline HbA1c, one year HbA1c and centre were included as covariates for the 7 mean effect on HbA1c after two years. HbA1c in the previous year was used as a covariate for the 8 dispersion parameters. The standard errors of both statistical models were adjusted for clustering 9 within each DAFNE course. Due to presence of treatment switching, an individual's HbA1c was 10 assumed to change as thought they had been allocated to the other trial arm. Therefore, the beta 11 regressions were estimated in the per protocol population (switchers excluded) rather than the 12 intention to treat (ITT) population (switchers included in their randomised arm). A sensitivity analysis 13 was conducted in which the beta regressions were estimated in the ITT population. 14 The results of the beta regression in the intention to treat population is given in Table 14 and the beta  15 regression estimated in the per protocol population is given in Table 15. The results of these beta 16 regressions are not easily interpretable as changes in HbA1c, as a logit link function is used to 17 estimate the mean effect parameter and the natural logarithm of the dispersion parameter is estimated 18 instead of the dispersion parameter itself. 19 Missing data was observed for HbA1c values in the per protocol population at 6 months (2.1% 20 missing), 1 year (4.2% missing) and 2 years (4.2% missing). A multiple imputation procedure was 21 employed in individuals with at least one HbA1c value (at 6 or 12 months) after randomisation but no 22 HbA1c value at 24 months. In line with the statistical analysis plan, missing 24 month HbA1c data 23 was imputed by multiple imputation using chained equations (regression) based on 10 imputed data 24 sets with baseline, 6 and 12 months HbA1c measurements, DAFNE course, centre, age, sex, and HFS 25 worry as covariates, if a participant had some HbA1c follow-up data. This imputation procedure was 26 conducted in the ITT population, prior to running the beta regressions. After imputation 236 out of 27 236 participants in the per protocol population and 259 out of 260 participants in the ITT population 28 had HbA1c follow up data. 29 The uncertainty in the parametric survival curves was included in the model's probabilistic sensitivity 30 analysis using a multivariate normal distribution. The variance-covariance matrices and the predicted 31 coefficients for each of the beta regressions were used to parameterise the multivariate normal 32 distributions. The coefficients are given in Table 14 and Table 15 respectively and the variance -33 covariance matrices for each of the beta regressions is given in          DKA were collected on an ongoing basis throughout the trial. Self-reported information was also 4 collected on the incidence of DKA. A summary of the numbers of DKAs and severe hypoglycaemic 5 events is given in Table 20. It can be seen that the number of DKAs and severe hypoglycaemic 6 events declines in the second year on every measure, except self-reported DKAs in the MDI+DAFNE 7 arm were the number events was the same in both years. As such, the statistical models used in the 8 economic data estimated the incidence of severe hypoglycaemia and DKA in the first and second 9 years separately. 10 Negative binomial regressions were used to predict the number of DKAs, and severe hypoglycaemic 1 events in years 1 and 2 for each outcome separately. When the outcome variable was the number of 2 severe hypos in year 1, year 1 HbA1c and treatment group were included as covariates. When the 3 outcome variable was the number of severe hypos in year 2, year 2 HbA1c and treatment group were 4 included as covariates When the outcome variable was the number of DKAs in year 1, year 1 HbA1c 5 and treatment group were included as covariates When the outcome variable was the number of 6 DKAs in year 2, year 2 HbA1c and treatment group were included as covariates. The possibility of 7 using the number of events in the previous year, baseline events for the 1 year outcomes and year 1 8 events for the 2 year outcomes, as a covariate was explored. However, due to the low number of 9 events, the negative binomial models often did not converge when this was included as a covariate. 10 The statistical models did not converge for DKAs reported as serious adverse events in the first year. 11 This was not the case for self-reported DKAs and there were more self-reported cases of DKA than 12 were picked up through the reporting of serious adverse events. Therefore, the rates of DKA were 13 estimated using self-reported DKAs as the outcome measure. 14 The statistical models were fitted using the Zellig package in R version3.2.0 and using specifications 15 described above; it was used to simulate the predicted number of severe hypoglycaemia and DKA 16 events in each trial arm 10,000 times. The simulations were separately in each trial arm and for 17 HbA1c values every 0.1% between 4% and 20.5%. The number of events observed in the simulations 18 was truncated at 20 events per year to reduce the effect of extreme values in the simulation on the 19 cost-effectiveness results. These simulations were then used to determine the probability that an 20 individual would suffer a given number of severe hypoglycaemic events and DKA events in a year, 21 dependent on their HbA1c that year and the trial arm they were allocated to. The probability that an 22 individual would suffer a given number of events was a fixed parameter in the PSA, therefore any 23 differences in the rates of DKA or severe hypoglycaemia for an individual between any two model 24 runs will solely be due to differences in their HbA1c. 25 The results of the negative binomial regressions are given in Table 21 and Table 22.   The cost of insulin, diabetes related contacts and insulin pumps used in the Sheffield Type 1 37 Diabetes model 38 Self-reported information was collected on individual's use of insulin, face to face contacts related to 39 their diabetes with a health care professional and telephone contacts related to their diabetes with a 40 health care professional. Data collected on insulin use included, the type of insulin used, the dose of 41 insulin and the method of insulin delivery. Ongoing information was also collected on whether an 42 individual switched their insulin delivery mechanism (either pumps or MDI). The cost of the diabetes 43 related contacts were sourced from the NHS reference costs and were £105.49 for face to face 44 contacts and £75.80 for telephone contacts. 8 Insulin costs were microcosted using the data in the 45 british national formularly and a prescription costs analysis, the full list of unit costs is given in Table  46 23. 9 10 The unit costs of the pumps and their related consumables was obtained from a survey of the 47 prices paid at the REPOSE trial sites. 48  The cost of insulin, diabetes related contacts and insulin pumps (including consumables) for insulin 54 pump therapy individuals were based on resource use data from the REPOSE trial data. It is expected 55 that the covariates which predict the cost of insulin in year 1 may be correlated with the covariates 56 which predict the cost of insulin in year 2. It is also expected that this may be true for the cost of 57 diabetes related contacts and the cost of insulin pumps (including consumables). Therefore, instead of 58 fitting six independent regression models, three seemingly unrelated regressions were fitted (one 59 seemingly unrelated regression for the cost of insulin, another for the cost of diabetes related contacts 60 and finally one for the cost of insulin pumps (including consumables). 61 In the cost insulin seemingly unrelated regression model, the cost of insulin in year 1 and the cost of 62 insulin in year 2 were used as the outcome variables for the seemingly unrelated regression model. 63 Baseline In the cost of insulin pump seemingly unrelated regression model, the cost of insulin pumps and 85 consumables in year 1 and the cost insulin pumps and consumables in year 2 were the two outcome 86 variables used in the model.